基于多模态注意力融合网络的英语口语自动评分

Automatic Rating of Spoken English Based on Multimodal Attention Fusion Network

针对传统英文口语评分效率及准确率低的问题,提出一种多模态注意力融合网络架构,加快模型训练效率及口语评分准确率。综合考虑口语发音的韵律声音特征及所答问题文本信息,从而提高网络鲁棒性。通过仿真,将该模型与LSTM、BiLSTM、GRU网络模型进行比较,所提出模型分数估计准确率为96.8%,明显高于其他方法。仿真结果表明:所提方法能够大幅减少评分时间,提高评分效率。

Aiming at the low efficiency and accuracy of traditional oral English scoring,a multimodal attention fusion network architecture is proposed to speed up the training efficiency of the model and the accuracy of oral English scoring.The network robustness is improved by comprehensively considering the prosodic sound characteristics of the spoken language pronunciation and the text information of the answered question.Through simulation,the proposed model is compared with LSTM,BiLSTM and GRU network model,and the score estimation accuracy of the proposed model is 96.8%,which is significantly higher than other methods.The simulation results show that the proposed method can significantly reduce the scoring time and improve the scoring efficiency.